---
id: domain
sidebar_label: Domain
title: Domain
abstract: The domain defines the universe in which your assistant operates. It specifies the intents, entities, slots, responses, forms, and actions your bot should know about. It also defines a configuration for conversation sessions.
---

Here is a full example of a domain, taken from the
[concertbot](https://github.com/RasaHQ/rasa/tree/main/examples/concertbot) example:

```yaml-rasa (docs/sources/examples/concertbot/domain.yml)
```

## Multiple Domain Files

The domain can be defined as a single YAML file or split across multiple files in a directory.
When split across multiple files, the domain contents will be read and automatically merged together.

Using the [command line interface](./command-line-interface.mdx#rasa-train),
you can train a model with split domain files by running:

```bash
rasa train --domain path_to_domain_directory
```

## Intents

The `intents` key in your domain file lists all intents
used in your [NLU data](./nlu-training-data.mdx) and [conversation training data](./training-data-format.mdx#conversation-training-data).

### Ignoring Entities for Certain Intents

To ignore all entities for certain intents, you can
add the `use_entities: []` parameter to the intent in your domain
file like this:

```yaml-rasa
intents:
  - greet:
      use_entities: []
```

To ignore some entities or explicitly take only certain entities
into account you can use this syntax:

```yaml-rasa
intents:
- greet:
    use_entities:
      - name
      - first_name
    ignore_entities:
      - location
      - age
```

Excluded entities for those intents will be unfeaturized and therefore
will not impact the next action predictions. This is useful when you have
an intent where you don't care about the entities being picked up.

If you list your intents without this parameter, the entities will be
featurized as normal.

:::note
If you want these entities not to influence action prediction,
set the [`influence_conversation: false`](./domain.mdx#slots-and-conversation-behavior)
parameter for slots with the same name.

:::

## Entities

The `entities` section lists all entities that can be
extracted by any [entity extractor](./components.mdx) in your
NLU pipeline.
If you are using the feature [Entity Roles and Groups](./nlu-training-data.mdx#entities-roles-and-groups) you also
need to list the roles and groups of an entity in this section.

For example:

```yaml-rasa
entities:
   - PERSON           # entity extracted by SpacyEntityExtractor
   - time             # entity extracted by DucklingEntityExtractor
   - membership_type  # custom entity extracted by DIETClassifier
   - priority         # custom entity extracted by DIETClassifier
   - city:            # custom entity extracted by DIETClassifier
       roles:
       - from
       - to
   - topping:         # custom entity extracted by DIETClassifier
       groups:
       - 1
       - 2
   - size:            # custom entity extracted by DIETClassifier
       groups:
       - 1
       - 2
```

## Slots

Slots are your bot's memory. They act as a key-value store
which can be used to store information the user provided (e.g their home city)
as well as information gathered about the outside world (e.g. the result of a
database query).

Slots are defined in the slots section of your domain with their name,
[type](domain.mdx#slot-types) and if and how they should [influence the assistant's
behavior](domain.mdx#slots-and-conversation-behavior).
The following example defines a slot with name "slot_name" and type `text`.

```yaml
slots:
  slot_name:
    type: text
```

### Slots and Conversation Behavior

You can specify whether or not a slot influences the conversation with the
`influence_conversation` property. 

If you want to store information in a slot without it influencing the conversation,
set `influence_conversation: false` when defining your slot. 

The following example defines a slot `age` which will store information about the
user's age, but which will *not* influence the flow of the conversation. This means
that the assistant will ignore the value of the slot each time it predicts the next action.

```yaml
slots:
  age:
    type: text
    # this slot will not influence the predictions
    # of the dialogue policies
    influence_conversation: false
```

When defining a slot, if you leave out `influence_conversation` or set it to `true`,
that slot will influence the next action prediction, unless it has slot type `any`.
The way the slot influences the conversation
will depend on its [slot type](./domain.mdx#slot-types).

The following example defines a slot `home_city` that influences the conversation. 
A [`text` slot](domain.mdx#text-slot) will
influence the assistant's behavior depending on whether the slot has a value.
The specific value of a `text` slot (e.g. *Bangalore* or *New York* or *Hong Kong*)
doesn't make any difference. 

```yaml
slots:
  # this slot will influence the conversation depending on
  # whether the slot is set or not
  home_city:
    type: text
    influence_conversation: true
```

As an example, consider the two inputs "What is the weather like?" and "What is the
weather like in Bangalore?" The conversation should diverge based on whether
the `home_city` slot was set automatically by the NLU. If the slot is already set, the bot
can predict the `action_forecast` action. If the slot is not set, it needs to get the `home_city`
information before it is able to predict the weather.

### Slot Types


#### Text Slot

* **Type**

  `text`

* **Use For**

  Storing text values.

* **Example**

  ```yaml-rasa
  slots:
     cuisine:
        type: text
  ```

* **Description**

  If `influence_conversation` is set to `true`, the assistant's behavior will change
  depending on whether the slot is set or not. Different texts do not influence the
  conversation any further. This means the following two stories are equal:

  ```yaml-rasa
  stories:
  - story: French cuisine
    steps:
    - intent: inform
    - slot_was_set:
      - cuisine: french

  - story: Vietnamese cuisine
    steps:
    - intent: inform
    - slot_was_set:
      - cuisine: vietnamese
  ```

#### Boolean Slot

* **Type**

  `bool`

* **Use For**

  Storing `true` or `false` values.

* **Example**

  ```yaml-rasa
  slots:
     is_authenticated:
        type: bool
  ```

* **Description**

  If `influence_conversation` is set to `true`, the assistant's behavior will change
  depending on whether the slot is empty, set to `true` or set to `false`. Note that an
  empty `bool` slot influences the conversation differently than if the slot was set to
  `false`.

#### Categorical Slot

* **Type**

  `categorical`

* **Use For**

  Storing slots which can take one of N values.

* **Example**

  ```yaml-rasa
  slots:
    risk_level:
      type: categorical
      values:
        - low
        - medium
        - high
  ```

* **Description**

  If `influence_conversation` is set to `true`, the assistant's behavior will change
  depending on the concrete value of the slot. This means the assistant's behavior is
  different depending on whether the slot in the above example has the value `low`,
  `medium`, or `high`.

  A default value `__other__` is automatically added to the user-defined
  values. All values encountered which are not explicitly defined in the slot's `values`
  are mapped to `__other__`.
  `__other__` should not be used as a user-defined value; if it is, it
  will still behave as the default to which all unseen values are mapped.

#### Float Slot

* **Type**

  `float`

* **Use For**

  Storing real numbers.

* **Example**

  ```yaml-rasa
  slots:
    temperature:
      type: float
      min_value: -100.0
      max_value:  100.0
  ```

* **Defaults**

  `max_value=1.0`, `min_value=0.0`

* **Description**

  If `influence_conversation` is set to `true`, the assistant's behavior will change
  depending on the value of the slot. If the value is between `min_value` and
  `max_value`, the specific value of the number is used.
  All values below `min_value` will be treated as `min_value`, and all values above
  `max_value` will be treated as `max_value`. Hence, if `max_value` is set to `1`,
  there is no difference between the slot values `2` and `3.5`.

#### List Slot

* **Type**

  `list`

* **Use For**

  Storing lists of values.

* **Example**

  ```yaml-rasa
  slots:
    shopping_items:
      type: list
  ```

* **Description**

  If `influence_conversation` is set to `true`, the assistant's behavior will change
  depending on whether the list is empty or not. The length of the list stored in
  the slot does not influence the dialogue. It only matters whether list length is zero or non-zero.

#### Any Slot

* **Type**

  `any`

* **Use For**

  Storing arbitrary values (they can be of any type, such as dictionaries or lists).

* **Example**

  ```yaml-rasa
  slots:
    shopping_items:
      type: any
  ```

* **Description**

  Slots of type `any` are always ignored during conversations. The property
  `influence_conversation` cannot be set to `true` for this slot type. If you want to
  store a custom data structure which should influence the conversation, use a
  [custom slot type](domain.mdx#custom-slot-types).

#### Custom Slot Types

Maybe your restaurant booking system can only handle bookings
for up to 6 people. In this case you want the *value* of the
slot to influence the next selected action (and not just whether
it's been specified). You can do this by defining a custom slot class.

The code below defines a custom slot class called `NumberOfPeopleSlot`. 
The featurization defines how the value of this slot gets converted to a vector
so Rasa Open Source machine learning model can deal with it.
The `NumberOfPeopleSlot` has three possible “values”, which can be represented with
a vector of length `2`.

|        |                |
|--------|----------------|
|`(0,0)` |not yet set     |
|`(1,0)` |between 1 and 6 |
|`(0,1)` |more than 6     |

```python title="my_custom_slots.py"
from rasa.shared.core.slots import Slot

class NumberOfPeopleSlot(Slot):

    def feature_dimensionality(self):
        return 2

    def as_feature(self):
        r = [0.0] * self.feature_dimensionality()
        if self.value:
            if self.value <= 6:
                r[0] = 1.0
            else:
                r[1] = 1.0
        return r
```

You can implement a custom slot class as an independent python module, 
separate from custom action code. Save the code for your custom slot in a directory 
alongside an empty file called "\_\_init\_\_.py" so that it will be recognized as a python module.
You can then refer to the custom slot class by it's module path.

For example, say you have saved the code above in "addons/my_custom_slots.py", a directory relative to your bot project:

```bash
└── rasa_bot
    ├── addons
    │   ├── __init__.py
    │   └── my_custom_slots.py
    ├── config.yml
    ├── credentials.yml
    ├── data
    ├── domain.yml
    ├── endpoints.yml
```

Your custom slot type's module path
is then `addons.my_custom_slots.NumberOfPeopleSlot`.
Use the module path to refer to the custom slot type in your domain file:

```yaml-rasa title="domain.yaml"
slots:
  people:
    type: addons.my_custom_slots.NumberOfPeopleSlot
    influence_conversation: true
```

Now that your custom slot class can be used by Rasa Open Source, add training stories that diverge based on the value of the `people` slot.
You could write one story for the case where `people` has a value between 1 and 6, and one for a value greater than six. You can choose any value within these ranges to put in your stories, since they are all featurized the same way (see the featurization table above).


```yaml-rasa
stories:
- story: collecting table info
  steps:
  # ... other story steps
  - intent: inform
    entities:
    - people: 3
  - slot_was_set:
    - people: 3
  - action: action_book_table

- story: too many people at the table
  steps:
  # ... other story steps
  - intent: inform
    entities:
    - people: 9
  - slot_was_set:
    - people: 9
  - action: action_explain_table_limit
```

### Slot Auto-fill

The slot will be set automatically if your NLU model picks up an entity, and your domain
 contains a slot with the same name, provided the following conditions are met:
 
1. `store_entities_as_slots` is set to true
2. the slot's `auto_fill` property is set to true


For example:

```yaml-rasa
stories:
- story: entity slot-filling
  steps:
  - intent: greet
    entities:
    - name: Ali
  - slot_was_set:
    - name: Ali
  - action: utter_greet_by_name
```

In this case, you don't have to include the `slot_was_set` part in the
story, because it is automatically picked up:

```yaml-rasa
stories:
- story: entity slot-filling
  steps:
  - intent: greet
    entities:
    - name: Ali
  - action: utter_greet_by_name
```


:::note Auto-filled slots & `influence_conversation`
An auto-filled slot that is defined with `influence_conversation: true` will influence the conversation the same way any other slot does.

In the example above, if the `name` slot's type is `text`, then it only matters that some name was detected, but it won't matter which one.
If the `name` slot is type `categorical`, then the behaviour will vary depending on the categories you have defined for the slot. 

Explicitly including a `slot_was_set` step in your story can make the behaviour of an
auto-filled slot that influences the conversation clearer, and will not change the behaviour of your story.

:::



To disable auto-filling for a particular slot, you can set the
`auto_fill` attribute to `false` in the domain file:

```yaml-rasa
slots:
  name:
    type: text
    auto_fill: false
```

### Initial slot values

You can provide an initial value for a slot in your domain file:

```yaml-rasa
slots:
  num_fallbacks:
    type: float
    initial_value: 0
```

## Responses

Responses are actions that send a message to a user without running any custom code or
returning events. These responses can be defined directly in the domain file under the `responses` key
and can include rich content such as buttons and attachments. For more information on responses and how to define them,
see [Responses](./responses.mdx).


## Forms

Forms are a special type of action meant to help your assistant collect information from a user.
Define forms under the `forms` key in your domain file.
For more information on form and how to define them, see [Forms](./forms.mdx).


## Actions

[Actions](./actions.mdx) are the things your bot can actually do.
For example, an action could:

* respond to a user,

* make an external API call,

* query a database, or

* just about anything!

All [custom actions](./custom-actions.mdx) should be listed in your domain, except responses which need not be listed
under `actions:` as they are already listed under `responses:`.

## Session configuration

A conversation session represents the dialogue between the assistant and the user.
Conversation sessions can begin in three ways:

1. the user begins the conversation with the assistant,

2. the user sends their first message after a configurable period of inactivity, or

3. a manual session start is triggered with the `/session_start` intent message.

You can define the period of inactivity after which a new conversation
session is triggered in the domain under the `session_config` key.

Available parameters are:
- `session_expiration_time` defines the time of inactivity in minutes after which a
new session will begin.
- `carry_over_slots_to_new_session` determines whether
existing set slots should be carried over to new sessions.

The default session configuration looks as follows:

```yaml-rasa
session_config:
  session_expiration_time: 60  # value in minutes, 0 means infinitely long
  carry_over_slots_to_new_session: true  # set to false to forget slots between sessions
```

This means that if a user sends their first message after 60 minutes of inactivity, a
new conversation session is triggered, and that any existing slots are carried over
into the new session. Setting the value of `session_expiration_time` to `0` means
that sessions will not end (note that the `action_session_start` action will still
be triggered at the very beginning of conversations).

:::note
A session start triggers the default action `action_session_start`. Its default
implementation moves all existing slots into the new session. Note that all
conversations begin with an `action_session_start`. Overriding this action could
for instance be used to initialize the tracker with slots from an external API
call, or to start the conversation with a bot message. The docs on
[Customizing the session start action](./default-actions.mdx#customization) shows you how to do that.

:::

## Config

The `config` key in the domain file maintains the `store_entities_as_slots` parameter.
When an entity is recognized by the NLU model and the entity name matches a slot name,
`store_entities_as_slots` defines whether the entity value should be placed in that slot.
By default, entities will auto-fill slots of the same name.

You can turn off all slot auto-filling by setting the `store_entities_as_slots` parameter to `false`:

```yaml-rasa title="domain.yml"
config:
  store_entities_as_slots: false
```

You can also turn off this behavior for a specific slot using the [`auto_fill`](./domain.mdx#slot-auto-fill)
parameter in that slot's definition.

:::note looking for config.yml?
If you're looking for information on the `config.yml` file, check out the docs on
[Model Configuration](./model-configuration.mdx).
:::
